Bibliographic record
Abstract
Objective To investigate and analyze the mental crisis status among university students.Methods Self-made questionnaire was used among 1 390 first-to third-year university students recruited by random sampling.Results ① 44.4% of the inquired students reported that they had experienced mental crises in university lives,with occurrence rate having nothing to do with gender.② The frequencies of events that trigger off mental crisis,from the high to the low,ranked in the following order: study,human communication,love,adaptation of freshmen,job,etc.③When getting into mental crisis,59.6% of students had ever asked others for help,although 38.7% failed to seek social back.School-mates,friends and family members were the main anticipated helpers.④ Nearly a quarter of the students who were in mental crisis had got ideation or behavior of killing(hurting) themselves or others.⑤ Nearly 30% of the students who had experienced mental crises reported that the crises had left shadow or scar in their hearts.They even admitted that the crises had directly resulted in the occurrence of mental difficulties or bad events.Conclusion Mental crisis has become a common phenomenon among the university students.Study and human communication have been the main life events which trigger off mental crises.Institutional administrators should highlight and improve students′ awareness and ability of seeking help,and prevent the potential harm that mental crisis might cause.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".